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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code>.permutation_importance</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-inspection-permutation-importance">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.inspection.permutation_importance</span></code></a></li>
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  <div class="section" id="sklearn-inspection-permutation-importance">
<h1><a class="reference internal" href="../classes.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a>.permutation_importance<a class="headerlink" href="#sklearn-inspection-permutation-importance" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.inspection.permutation_importance">
<code class="sig-prename descclassname">sklearn.inspection.</code><code class="sig-name descname">permutation_importance</code><span class="sig-paren">(</span><em class="sig-param">estimator</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em>, <em class="sig-param">scoring=None</em>, <em class="sig-param">n_repeats=5</em>, <em class="sig-param">n_jobs=None</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/inspection/_permutation_importance.py#L45"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.inspection.permutation_importance" title="Permalink to this definition">¶</a></dt>
<dd><p>Permutation importance for feature evaluation <a class="reference internal" href="#rd9e56ef97513-bre" id="id1">[BRE]</a>.</p>
<p>The <a class="reference internal" href="../../glossary.html#term-estimator"><span class="xref std std-term">estimator</span></a> is required to be a fitted estimator. <code class="docutils literal notranslate"><span class="pre">X</span></code> can be the
data set used to train the estimator or a hold-out set. The permutation
importance of a feature is calculated as follows. First, a baseline metric,
defined by <a class="reference internal" href="../../glossary.html#term-scoring"><span class="xref std std-term">scoring</span></a>, is evaluated on a (potentially different)
dataset defined by the <code class="docutils literal notranslate"><span class="pre">X</span></code>. Next, a feature column from the validation set
is permuted and the metric is evaluated again. The permutation importance
is defined to be the difference between the baseline metric and metric from
permutating the feature column.</p>
<p>Read more in the <a class="reference internal" href="../permutation_importance.html#permutation-importance"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>estimator</strong><span class="classifier">object</span></dt><dd><p>An estimator that has already been <a class="reference internal" href="../../glossary.html#term-fitted"><span class="xref std std-term">fitted</span></a> and is compatible
with <a class="reference internal" href="../../glossary.html#term-scorer"><span class="xref std std-term">scorer</span></a>.</p>
</dd>
<dt><strong>X</strong><span class="classifier">ndarray or DataFrame, shape (n_samples, n_features)</span></dt><dd><p>Data on which permutation importance will be computed.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like or None, shape (n_samples, ) or (n_samples, n_classes)</span></dt><dd><p>Targets for supervised or <code class="docutils literal notranslate"><span class="pre">None</span></code> for unsupervised.</p>
</dd>
<dt><strong>scoring</strong><span class="classifier">string, callable or None, default=None</span></dt><dd><p>Scorer to use. It can be a single
string (see <a class="reference internal" href="../model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a>) or a callable (see
<a class="reference internal" href="../model_evaluation.html#scoring"><span class="std std-ref">Defining your scoring strategy from metric functions</span></a>). If None, the estimator’s default scorer is used.</p>
</dd>
<dt><strong>n_repeats</strong><span class="classifier">int, default=5</span></dt><dd><p>Number of times to permute a feature.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, default=None</span></dt><dd><p>The number of jobs to use for the computation.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance, or None, default=None</span></dt><dd><p>Pseudo-random number generator to control the permutations of each
feature. See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">random_state</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl>
<dt><strong>result</strong><span class="classifier">Bunch</span></dt><dd><p>Dictionary-like object, with attributes:</p>
<dl class="simple">
<dt>importances_mean<span class="classifier">ndarray, shape (n_features, )</span></dt><dd><p>Mean of feature importance over <code class="docutils literal notranslate"><span class="pre">n_repeats</span></code>.</p>
</dd>
<dt>importances_std<span class="classifier">ndarray, shape (n_features, )</span></dt><dd><p>Standard deviation over <code class="docutils literal notranslate"><span class="pre">n_repeats</span></code>.</p>
</dd>
<dt>importances<span class="classifier">ndarray, shape (n_features, n_repeats)</span></dt><dd><p>Raw permutation importance scores.</p>
</dd>
</dl>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="rd9e56ef97513-bre"><span class="brackets"><a class="fn-backref" href="#id1">BRE</a></span></dt>
<dd><p>L. Breiman, “Random Forests”, Machine Learning, 45(1), 5-32,
2001. <a class="reference external" href="https://doi.org/10.1023/A:1010933404324">https://doi.org/10.1023/A:1010933404324</a></p>
</dd>
</dl>
</dd></dl>

<div class="section" id="examples-using-sklearn-inspection-permutation-importance">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.inspection.permutation_importance</span></code><a class="headerlink" href="#examples-using-sklearn-inspection-permutation-importance" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="In this example, we compute the permutation importance on the Wisconsin breast cancer dataset u..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png" src="../../_images/sphx_glr_plot_permutation_importance_multicollinear_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py"><span class="std std-ref">Permutation Importance with Multicollinear or Correlated Features</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we will compare the impurity-based feature importance of RandomForestClassifie..."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_permutation_importance_thumb.png" src="../../_images/sphx_glr_plot_permutation_importance_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py"><span class="std std-ref">Permutation Importance vs Random Forest Feature Importance (MDI)</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We are pleased to announce the release of scikit-learn 0.22, which comes with many bug fixes an..."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" src="../../_images/sphx_glr_plot_release_highlights_0_22_0_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py"><span class="std std-ref">Release Highlights for scikit-learn 0.22</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
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